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readiness Theory and application of machine learning and artificial intelligence including predictive maintenance, predictive logistics, statistical analysis of production data, statistical process control
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within the last 60 months or currently pursuing. Discipline(s): Chemistry and Materials Sciences (12 ) Communications and Graphics Design (6 ) Computer, Information, and Data Sciences (17 ) Earth and
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tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in precision agriculture and
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spectroradiometers. Ability to apply AI tools and machine learning for advanced image analysis, weed-crop detection, and mapping. Experience in data collection, processing, and interpretation. Strong background in
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(cattle and sheep). Through this process, the participant will acquire expertise in real-time pasture surveillance techniques for evaluating animal welfare (such as animal counts, weight, body temperature
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of incorporating sensors, spectroscopy, imaging, and machine learning techniques into the postharvest processing workflows and/or pre-harvest evaluation of food quality and safety. The participant will have the
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development and implementation of EEG-based, passive brain-computer interfaces (BCIs) for optimization of human-machine teams, such as exoskeletons for physical augmentation in defense applications. The aim